A solid state drive (SSD) is a data storage device that utilizes solid-state memory to retain data in nonvolatile memory chips. NAND-based flash memories are widely used as the solid-state memory storage in SSDs due to their compactness, low power consumption, low cost, high data throughput and reliability. SSDs commonly employ several NAND-based flash memory chips and a flash controller to manage the flash memory and to transfer data between the flash memory and a host computer.
While NAND-based flash memories are reliable, they are not inherently error-free and often rely on error correction coding (ECC) to correct raw bit errors in the stored data. One commonly employed error correction code employed in nonvolatile memory storage modules, such as SSDs, are low-density parity-check (LDPC) codes. An LDPC code is a linear error correcting code having a parity check matrix with a small number of nonzero elements in each row and column. Various methods for decoding data encoded with LDPC error correction codes are known in the art. Some commonly employed decoding methods for LDPC coding are approximations of the quasi-optimal decoding method of belief propagation. Such approximations of belief propagation can introduce hardware simplifications in the design of a decoder. The layered min-sum algorithm (MSA), as well as others, are iterative by layer and are approximations of belief propagation. The layered MSA is less complex than other decoding methods known in the art. However, approximations to belief propagation such as layered MSA exhibit noticeable degradation in the decoding performance compared to the more complex decoding methods, such as belief propagation. To counteract the degradation in the decoding performance achievable with approximations such as the layered MSA, normalized layered min-sum algorithms have been developed incorporating a normalization factor, or attenuation factor, to account for the degradation in decoding performance. However, in the prior art, normalized layered MSAs utilize the same attenuation factor for each layer of the min-sum decoding process to recover the LDPC encoded codeword. Utilization of the same attenuation factor for each layer of an algorithmic approximation to belief propagation may not converge to the original codeword or may require a high number of iterations for convergence. This may present a problem.
Use of belief propagation directly as the decoding algorithm while optimal in terms of error correction capacity does not always converge quickly to produce a codeword. This can present a problem.
In standard normalized layered min-sum LDPC decoding, the attenuation factor is not subject to change during the decoding procedure, the attenuation factor remains the same regardless of the layer of the structure upon which the min-sum calculation is being performed. Utilizing the same attenuation factor for all the layers during the decoding procedure may not yield the best decoding performance, because the calculations for the second layer already utilize an input which is cleaner than the input used during the calculations on the first layer. This may present a problem.
In the standard normalized min-sum algorithm, the attenuation factor is computed by comparing the value obtained with the belief propagation algorithm and the min-sum algorithm for the entire parity check matrix associated with a codeword. In other words, the attenuation factor is computed as the ratio of the average value in check node processing computed with belief propagation and the average value in check node processing computed with the min-sum algorithm. This may present a problem.